Search results for: feature expanding.

Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Hamza Nejib, Okba Taouali

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This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: fractional differential (FD), computed tomography (CT), fusion, aplha, texture features.

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Authors: Samir Khene

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Present and future industrial uses of high critical temperature superconductors require high critical temperatures TC and strong current densities JC. These two aims constitute the two motivations of the scientific research in this domain. The most significant feature of any superconductor, from the viewpoint of uses, is the maximum electrical transport current density that this superconductor is capable of withstanding without loss of energy. In this work, vortices pinning in conventional and high-TC superconductors will be studied. Our experiments on vortices pinning in single crystals and nanoparticles of YBa2Cu3O7- and La1.85 Sr0.15CuO will be presented. It will be given special attention to the study of the YBa2Cu3O7- nanoparticles doped by ferromagnetic nanoparticles of Y3Fe5O12. The ferromagnetism and superconductivity coexistence in this compound will be demonstrated, and the influence of these ferromagnetic nanoparticles on the variations of the critical current density JC in YBa2Cu3O7- nanoparticles as a function of applied field H and temperature T will be studied.

Keywords: ferromagnetism, superconductivity, coexistence, magnetic material

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Authors: Diego Carrasco, Catarina Freitas, Hugo Rio Tinto, Ricardo Rio Tinto, Nuno Couto, Joaquim Gago, Carlos Carvalho

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Introduction: Endoscopic ultrasound-guided choledochoduodenostomy (EUS-CD) to drain the gallbladder can be a palliative care procedure for non-surgical oncologic patients with cholelithiasis and cholangitis process. Case description: A 59-years old Caucasian male diagnosed with extrahepatic cholangiocarcinoma with multiple liver, lung and peritoneum metastasis, unresponsive to treatment with gemcitabine/cisplatin, presented in the institution with fever, hypotension, and severe upper right abdominal pain secondary to cholelithiasis and cholangitis process. The patient was admitted and started on large spectrum antibiotics plus fluid-challenge. Afterward, a percutaneous transhepatic biliary drainage (PTBD) was performed to drain the gallbladder. This procedure temporarily stabilized the patient. However, the definitive solution required gallbladder removal. Since the patient exhibited an advanced oncologic disease and poor response to the chemotherapy, he was not a candidate for surgical intervention. Diagnostic Pathways: A self-expanding metal stent was placed from the duodenum into the bile duct by endoscopic ultrasound-guided. The stent allowed efficient drainage of the contrast from the gallbladder at the end of the endoscopic procedure. Conclusion and Discussion: The stent allowed efficient drainage of the contrast from the gallbladder at the end of the endoscopic procedure and successfully reversed the cholangitis process. EUS-CD is an effective and safe technique and can be used as a palliative care procedure for non-surgical oncologic patients.

Keywords: palliative care, cholangiocarcinoma, choledochoduodenostomy, endoscopic ultrasound-guided

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Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

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In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Rajendra Singh, Krishna Mondal, Chandranath Chatterjee

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The water, energy, and food (WEF) nexus describes the interconnectedness of these three essential elements of human life. Each of these three sectors depends on the others. India's expanding population, urbanization, and industrialization make WEF nexus management difficult. Coupling and coordination degrees can be used as indicators of a complex system's level of sustainable development. Thus, coupling and coordination of WEF sectors in India are essential for achieving Sustainable Development Goals (SDGs) 2 (zero hunger), 6 (clean water and sanitation), and 7 (affordable and clean energy). This study used a newly developed WEF nexus model and the concept of coupling coordination degree model to examine the coupling and coordination degrees of the WEF nexus at India's sub-national scale (States/Union Territories (UTs)) for the years 2011 and 2021. Results indicate that the WEF nexus coupling degree was reasonably stable among the Indian States/UTs in both years, with all having a coupling degree above 0.90, indicating high-quality coupling. However, the degree of coordination varied spatially and temporally from ‘primary development’ to ‘quality development’ for the Indian States/UTs. In 2021, it went from 53% to 14% intermediate development and 44% to 83% good development compared to 2011. Most Indian States/UTs developed SDG2 more than SDG6 and SDG7. This study also suggests that most States/UTs must implement WEF-related policies and programmes effectively to achieve quality coordinated WEF nexus development. This study may help administrators and policymakers identify States/UTs that need more attention to implement existing or new policies for achieving SDGs 2, 6, and 7.

Keywords: WEF nexus model, Pardee-RAND WEF nexus, sustainable development, policy

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Authors: Minbale Aschale, Bitew K. Dessie, Endaweke Assegide, Yosef Abebe, Tena Alamirew, Claire L. Walsh, Gete Zeleke

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The upper Awash Basin faces pollution challenges due to urbanization, population growth, and expanding industries. It receives various pollutants from its catchments. The study aimed to assess the impact of wastewater irrigation on vegetables and inform stakeholders about pollution challenges and consequences. Eighty-two composite samples of matured vegetables were randomly collected from twenty-one agricultural farm sites. These samples were analyzed for potentially toxic elements, including Cd, Pb, Cr, Hg, As, Ni, Sr, B, Co, Cu, Mn, Fe, Zn, and Se. The results indicated significant variations in concentrations across different sites, with localized contributions from various contaminants. Cr, Cd, and Pb concentrations in most vegetables exceeded recommended levels. Pollution levels varied with metals and vegetable types. Different vegetables contribute differently to health risks. The relative contributions of Ethiopian kale, cabbage, red beet, lettuce, Swiss chard, Gurage cabbage, tomato, zucchini, carrot, onion, watermelon, and potato to the aggregated risk were 12.69%, 12.25%, 11.83%, 11.20%, 10.21%, 9.91%, 8.49%, 5.66%, 3.96%, 3.35%, 3.10%, and 2.72%, respectively. Comparison with permissible standards revealed inadequate environmental management by relevant regulatory bodies and industries. Despite good laws and standards at the federal and regional levels, they are ineffectively implemented or enforced to prevent environmental pollution. Mitigation measures are urgently recommended to address the potential health implications of toxic substances.

Keywords: pollution, upper Awash Basin, health risk, Ethiopia

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Authors: Lazaro E. Nnko, Japhet J. Kashaigili, Gerald C. Monela, Pantaleo K. T. Munishi

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Agroforestry systems and practices are perceived to improve livelihood and sustainable management of natural resources. However, their adoption in various regions differs with the biophysical conditions and societal characteristics. This study was conducted in Kilombero District to investigate the factors influencing the adoption of different agroforestry systems and practices in agro-ecosystems and farming systems. A household survey, key informant interviews, and focus group discussion was used for data collection in three villages. Descriptive statistics and multinomial logistic regression in SPSS were applied for analysis. Results show that Igima and Ngajengwa villages had home garden practices dominated, as revealed by 63.3% and 66.7%, respectively, while Mbingu village had mixed intercropping practice with 56.67%. Agrosilvopasture systems were dominant in Igima and Ngajengwa villages with 56.7% and 66.7%, respectively, while in Mbingu village, the dominant system was agrosilviculture with 66.7%. The results from multinomial logistic regression show that different explanatory variable was statistical significance as predictors of the adoption of agroforestry systems and practices. Residence type and sex were the most dominant factor influencing the adoption of agroforestry systems. Duration of stay in the village, availability of extension education, residence, and sex were the dominant factor influencing the adoption of agroforestry practices. The most important and statistically significant factors among these were residence type and sex. The study concludes that agroforestry will be more successful if the local priorities, which include social-economic need characteristics of the society, will be considered in designing systems and practices. The socio-economic need of the community should be addressed in the process of expanding the adoption of agroforestry systems and practices.

Keywords: agroforestry adoption, agroforestry systems, agroforestry practices, agroforestry, Kilombero

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Authors: Mohammadali Tavakoli, Atefeh Heydari, Zuriati Ismail, Naomie Salim

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With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a high-quality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods.

Keywords: fake reviews, feature collection, opinion spam, spam detection

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Authors: Annie Y. N. Cheng

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Since the handover of Hong Kong to China, 'National Identity' has become a vital focus promoted by the HKSAR government. According to the poll by the University of Hong Kong’s Public Opinion Programme (2010 – 2015), young people aged between 18 and 29 have the least and decreasing recognition, an average 5.5%, of their Chinese identity. Past research has shown that student participation in exchange programmes and study tours provides the possibility of new formulations of national identity. Since the Policy Address 2008, the HKSAR government has been actively expanding and exploring the feasibility of Mainland exchange programmes to enhance our youths’ understanding of Chineseness and to strengthen their national identity. Schools have been sponsored or subsidized with the costs of Mainland exchange activities through various grants and channels. Considering the significantly increasing number of Hong Kong youths who have participated in these Mainland exchange programmes and study tours, however, the effectiveness of these activities is understudied. At present, there is the lack of systematic research on the impacts of these activities and the ways in which they influence our students’ perceptions of national identity. Using case study approach, this study aims to examine students’ perceptions of their national identity; and evaluate whether the Mainland exchange programmes or study tours have influences on students’ perceptions of national identity. Results show that the influences on national identity varied which were dependent on the objectives and destinations of the programmes. The findings of this study can provide significant feedback for schools to organize meaningful Mainland exchange activities or study tours and inform policy makers how to formulate effective strategies for promoting such exchange activities.

Keywords: Hong Kong youth, mainland exchange programme, national identity, study tours

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Sebastião Pais, João Casal, Ricardo Ponciano, Sérgio Lorenço

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The adaptation paradigm is a basic defining feature for pervasive computing systems. Adaptation systems must work efficiently in a smart environment while providing suitable information relevant to the user system interaction. The key objective is to deduce the information needed information changes. Therefore relying on fixed operational models would be inappropriate. This paper presents a study on developing an Intelligent Personal Assistant to assist the user in interacting with their Smart Environment. We propose an Unsupervised and Language-Independent Adaptation through Intelligent Speech Interface and a set of methods of Acquiring Knowledge, namely Semantic Similarity and Unsupervised Learning.

Keywords: intelligent personal assistants, intelligent speech interface, unsupervised learning, language-independent, knowledge acquisition, association measures, symmetric word similarities, attributional word similarities

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Authors: Ting-Ting Wen, Jung-Ruey Tsai

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This paper reports a method for the replication of micro-scale structures. By using electromagnetic force-assisted imprinting system with magnetic soft stamp written square-microlens cavity, a photopolymer square-microlens structures can be rapidly fabricated. Under the proper processing conditions, the polymeric square-microlens structures with feature size of width 100.3um and height 15.2um across a large area can be successfully fabricated. Scanning electron microscopy (SEM) and surface profiler observations confirm that the micro-scale polymer structures are produced without defects or distortion and with good pattern fidelity over a 60x60mm2 area. This technique shows great potential for the efficient replication of the micro-scale structure array at room temperature and with high productivity and low cost.

Keywords: square-microlens structures, electromagnetic force-assisted imprinting, magnetic soft stamp

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Authors: Alexander Aurell

Abstract:

One of the latest trends in the modeling of human crowds is the mean-field game approach. In the mean-field game approach, the motion of a human crowd is described by a nonstandard stochastic optimal control problem. It is nonstandard since congestion is considered, introduced through a dependence in the performance functional on the distribution of the crowd. This study extends the class of mean-field pedestrian crowd models to allow for non-local congestion and arbitrary, but finitely, many interacting crowds. The new congestion feature grants pedestrians a 'personal space' where crowding is undesirable. The model is treated as a mean-field type game which is derived from a particle picture. This, in contrast to a mean-field game, better describes a situation where the crowd can be controlled by a central planner. The latter is suitable for decentralized situations. Solutions to the mean-field type game are characterized via a Pontryagin-type Maximum Principle.

Keywords: congestion, crowd dynamics, interacting populations, mean-field approximation, optimal control

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Authors: Khalid El Asri

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Culture influences lexicalization of salient concepts in a society. Hence, languages often have different degrees of equivalence regarding lexical items of different fields. The present study focuses on the field of emotions in English and Moroccan Arabic. Findings of a comparative study that involved fifty English emotions revealed that Moroccan Arabic has equivalence of some English emotion terms, partial equivalence of some emotion terms, and no equivalence for some other terms. It is hypothesized then that emotion terms that have near equivalence in Moroccan Arabic will be easier to acquire for EFL learners, while partially equivalent terms will be difficult to acquire, and those that have no equivalence will be even more difficult to acquire. In order to test these hypotheses, the participants (104 advanced Moroccan EFL learners and 104 native speakers of English) were given two tests: the first is a receptive one in which the participants were asked to choose, among four emotion terms, the term that is appropriate to fill in the blanks for a given situation indicating certain kind of feelings. The second test is a productive one in which the participants were asked to give the emotion term that best described the feelings of the people in the situations given. The results showed that conceptually equivalent terms do not pose any problems for Moroccan EFL learners since they can link the concept to an already existing linguistic category; whereas the results concerning the acquisition of partially equivalent terms indicated that this type of emotion terms were difficult for Moroccan EFL learners to acquire, because they need to restructure the boundaries of the target linguistic categories by expanding them when the term includes other range of meanings that are not subsumed in the L1 term. Surprisingly however, the results concerning the case of non-equivalence revealed that Moroccan EFL learners could internalize the target L2 concepts that have no equivalence in their L1. Thus, it is the category of emotion terms that have partial equivalence in the learners’ L1 that pose problems for them.

Keywords: acquisition, culture, emotion terms, lexical equivalence

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Authors: Sheba Akhtar

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The profound importance that Islam places on knowledge has directly influenced the architectural development of the mosque throughout the Muslim world. The masjid is the most important religious building type in Islamic society because, as a place of worship and social interaction, it is the center of both spiritual knowledge and secular guidance. The Quran begins with the emphatic injunction, “Iqra”, establishing the central importance of the pursuit of the sacred ilm that is offered to man by Allah. Similarly, numerous hadiths of the Prophet Muhammad emphasize the profound importance of the acquisition and dissemination of knowledge, both spiritual and temporal. The Muslim worshipper must enter the sacred space of the masjid to receive spiritual knowledge, but the transition from the profane realm outside the mosque to that of spirituality within is not merely physical; it is also deeply psychological and emotional. To this end, the architecture of the masjid, from the plan and geometry to the design elements and intricate ornamental details, plays a vital role in creating the environment within which the ritual acts of wudu and salat are enacted to foster the transformative journey, from the mundane reality of this world to the realm of spirituality beyond, in the heart, mind, and soul of the worshipper. It is expected that the paper will provide a better understanding of the ways in which sacred Islamic knowledge has influenced the architectural design of the mosque. The research will draw upon Islamic epistemology, Islamic architecture history, and compositional analysis to demonstrate this philosophical, historical, and formal relationship. In this way, the paper will provide a meaningful bridge between the existing knowledge related to mosque design and the expanding academic discourse about the religious architecture of Islam.

Keywords: Islamic architecture, mosque architecture, religious architecture, sacred architecture

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Authors: Nilubon Thongtan, Wilai Chiemchaisri, Chart Chiemchaisri

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Purpose of study is to investigate performance of two-stage membrane bioreactor (2S-MBR) to treat trimethoprim and sulfamethoxazole in solid waste leachate. This system consists of 2 tanks, anoxic tank with incline plates and MBR tank. The system was operated at 12 h-HRT each, of which the MBR MLSS concentration was operated at 25,000-35,000 mg/L. The average sCOD concentration of the fed leachate was 6,310±3,595 mg/L. It shows that high organic removals in terms of sCOD and sBOD were achieved as of 97-99% and 99%, respectively. The TKN and NH3-N removals were 76-98% and 91-99%, respectively. Concurrently, trimethoprim and sulfamethoxazole were detected in the leachate with concentrations of 113-0 μg/L and 74-2 μg/L, respectively. High removals of trimethoprim and sulfamethoxazole were also found as of 95-99% and 85-95%, respectively. In sum, this MBR feature and operation gave achievement in treatment of macro-pollutants including trimethoprim and sulfamethoxazole existing in low levels in the solid waste leachate.

Keywords: membrane bioreactor, solid waste leachate, sulfamethoxazole, trimethoprim

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Authors: Soad Abokhamis Mousavi

Abstract:

The Trombe wall is an ancient technique that continues to serve as an effective feature of a passive solar system. However, in practice, architects and their clients are not opting for the Trombe wall because of the view of the Trombe wall on the facades of the buildings. Therefore, this study has two main goals, and one of the goals is to find out why the Trombe wall is not considered in the buildings in the Mediterranean region. And the second goal is to find a solution to facilitate the societal acceptance of the Trombe walls in buildings. To cover the goals, the present work attempts to develop and design a different Trombe Wall with different Materials and views in the facades of the buildings. A qualitative data method was used in this article. The qualitative method was developed based on observation and questionnaires with different clients and expert architects in the selected region. Results indicate that the view of the Trombe wall in the facade of buildings can be used with different designs in order to not affect the beauty of the buildings.

Keywords: trombe wall, societal acceptance, building, energy efficacy

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Authors: Sebastião Pais, João Casal, Ricardo Ponciano, Sérgio Lourenço

Abstract:

The adaptation paradigm is a basic defining feature for pervasive computing systems. Adaptation systems must work efficiently in smart environment while providing suitable information relevant to the user system interaction. The key objective is to deduce the information needed information changes. Therefore, relying on fixed operational models would be inappropriate. This paper presents a study on developing a Intelligent Personal Assistant to assist the user in interacting with their Smart Environment. We propose a Unsupervised and Language-Independent Adaptation through Intelligent Speech Interface and a set of methods of Acquiring Knowledge, namely Semantic Similarity and Unsupervised Learning.

Keywords: intelligent personal assistants, intelligent speech interface, unsupervised learning, language-independent, knowledge acquisition, association measures, symmetric word similarities, attributional word similarities

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Authors: Yunzhe Tong, Jun Fan, Bin Wang

Abstract:

The vibroacoustic behavior of an immersed infinite cylindrical shell with periodic lengthwise ribs has been studied in this paper. The motions of the shell are described by the Donnell equations. Each lengthwise rib is modeled as an elastic beam. The motions of the bulkheads are decomposed into the longitudinal motions and flexural motions. The analytical expressions of the shell motions can be obtained through circumferential mode expansion, Fourier Transform and periodic boundary condition in the circumferential direction. Furthermore, the far-field radiated pressure has been obtained using the stationary phase. The analysis of wavenumber domain shows that periodic lengthwise stiffeners in the circumferential direction can produce flexural Bloch waves. The dominant feature in far-field pressure amplitude is the resonance of the supersonic components of the flexural Bloch waves in the circumferential direction.

Keywords: flexural Bloch wave, stiffened shell, vibroacoustics, wavenumber analysis

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Authors: Maamar Latroch, Mohamed Bourahla

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Forward topology used in switch mode power supply (SMPS) is one of the most famous configuration feeding DC systems such as telecommunication systems and other specific applications where the galvanic isolation is required. This configuration benefits of the high frequency feature of the transformer to provide a small size and light weight of the over all system. However, the stresses existing on the power switch during an ON/OFF commutation limit the transmitted power to the DC load. This paper investigates the main causes of the stresses in voltage existing during a commutation cycle and suggest a low cost solution that eliminates the overvoltage. As a result, this configuration will yield the possibility of the use of this configuration in higher power applications. Simulation results will show the efficiency of the presented method.

Keywords: switch mode power supply, forward topology, resonance topology, high frequency commutation

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Authors: Adeep Hande, Shubham Agarwal

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This paper presents a study on identifying sexism in online texts using various state-of-the-art deep learning models based on BERT. We experimented with different feature sets and model architectures and evaluated their performance using precision, recall, F1 score, and accuracy metrics. We also explored the use of pseudolabeling technique to improve model performance. Our experiments show that the best-performing models were based on BERT, and their multilingual model achieved an F1 score of 0.83. Furthermore, the use of pseudolabeling significantly improved the performance of the BERT-based models, with the best results achieved using the pseudolabeling technique. Our findings suggest that BERT-based models with pseudolabeling hold great promise for identifying sexism in online texts with high accuracy.

Keywords: large language models, semi-supervised learning, sexism detection, data sparsity

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Authors: Elyta Widyaningrum

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The LiDAR data acquisition has been recognizable as one of the fastest solution to provide the basis data for topographic base mapping in Indonesia. The challenges to accelerate the provision of large-scale topographic base maps as a development plan basis gives the opportunity to implement the automated scheme in the map production process. The one stop processing will also contribute to accelerate the map provision especially to conform with the Indonesian fundamental spatial data catalog derived from ISO 19110 and geospatial database integration. Thus, the automated LiDAR classification, DTM generation and feature extraction will be conducted in one GIS-software environment to form all layers of topographic base maps. The quality of automated topographic base map will be assessed and analyzed based on its completeness, correctness, contiguity, consistency and possible customization.

Keywords: automation, GIS environment, LiDAR processing, map quality

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Authors: Xiangyu Yang

Abstract:

Xiao Qian (1910-1999) was a prolific literary translator between Chinese and English in both directions and an influential commentator on Chinese translation practices for nearly 70 years (1931-1998). During his stay in Britain from 1939 to 1946, Xiao self-translated and published a series of short stories, essays, and feature articles. With Pedersen's theoretical framework, the paper finds that Xiao flexibly adopted seven translation strategies (i.e. phonemic retention, specification, direct translation, generalization, substitution, omission, and official equivalent) to deal with the expressions specific to Chinese culture, struggling to seek a balance between adequate translation and acceptable translation in a historical condition of the huge gap between China and the west in the early twentieth century. Besides, the study also discovers that Xiao's translation strategies were greatly influenced by his own translational purpose as well as the literary systems, ideologies, and patronage in China and Britain in the 1940s.

Keywords: self-translation, extralinguistic cultural reference, Xiao Qian, Pedersen

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Authors: Konstantinos C. Kartsakalis, Angeliki Skoura, Vasileios Megalooikonomou

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The fuzzy composition of objects depicted in images acquired through MR imaging or the use of bio-scanners has often been a point of controversy for field experts attempting to effectively delineate between the visualized objects. Modern approaches in medical image segmentation tend to consider fuzziness as a characteristic and inherent feature of the depicted object, instead of an undesirable trait. In this paper, a novel technique for efficient image retrieval in the context of images in which segmented objects are either crisp or fuzzily bounded is presented. Moreover, the proposed method is applied in the case of multiple, even conflicting, segmentations from field experts. Experimental results demonstrate the efficiency of the suggested method in retrieving similar objects from the aforementioned categories while taking into account the fuzzy nature of the depicted data.

Keywords: fuzzy object, fuzzy image segmentation, motion descriptors, MRI imaging, object-based image retrieval

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Ayesha Sohail, Khadija Maqbool, Anila Asif, Haroon Ahmad

Abstract:

The field of tissue engineering is an active area of research. Bone tissue engineering helps to resolve the clinical problems of critical size and non-healing defects by the creation of man-made bone tissue. We will design and validate an efficient numerical model, which will simulate the effective diffusivity in bone tissue engineering. Our numerical model will be based on the finite element analysis of the diffusion-reaction equations. It will have the ability to optimize the diffusivity, even at multi-scale, with the variation of time. It will also have a special feature, with which we will not only be able to predict the oxygen, glucose and cell density dynamics, more accurately, but will also sort the issues arising due to anisotropy. We will fix these problems with the help of modifying the governing equations, by selecting appropriate spatio-temporal finite element schemes, by adaptive grid refinement strategy and by transient analysis.

Keywords: scaffolds, porosity, diffusion, transient analysis

Procedia PDF Downloads 541

Authors: Mansour Al Qubeissi, Sergei S. Sazhin, Cyril Crua, Morgan R. Heikal

Abstract:

This paper presents the application of the Discrete Component Model for heating and evaporation to multi-component biodiesel fuel droplets in direct injection internal combustion engines. This model takes into account the effects of temperature gradient, recirculation and species diffusion inside droplets. A distinctive feature of the model used in the analysis is that it is based on the analytical solutions to the temperature and species diffusion equations inside the droplets. Nineteen types of biodiesel fuels are considered. It is shown that a simplistic model, based on the approximation of biodiesel fuel by a single component or ignoring the diffusion of components of biodiesel fuel, leads to noticeable errors in predicted droplet evaporation time and time evolution of droplet surface temperature and radius.

Keywords: heat/mass transfer, biodiesel, multi-component fuel, droplet

Procedia PDF Downloads 567